# define objects to save model estimates for plotting
diffs <- NA
diffs.se <- NA
beta1 <- beta2 <- NA
se1 <- se2 <- NA

year <- 1949:2010

# loop through all the different model specifications for this treaty variable
for(j in 1:length(year)){
  for(k in 1:2){
      # load both human rights latent variables from Fariss 2014
if(k==1)data <- read.csv("CYLatentRepressionDynamicStandardDynamicX02.csv")
if(k==2)data <- read.csv("CYLatentRepressionConstantStandardDynamicX02.csv")

OBS <- rev(cumsum(rev(as.numeric(table(data$YEAR)))))


# just leave this set to TRUE (see ModelTreatyDifferencesTREATYCOUNT.R file)
FULL <- TRUE

# load latent treaty data with latent treaty estimates included
treaty <- read.csv("CYEstimateLatentHRtreaty04.csv")

# create treaty proportion variable out of binary treaty variables
test <- treaty[,4:(ncol(treaty)-2)]
NA.test <- !is.na(test)

TREATY <- apply(test,1,sum,na.rm=T)
TOTAL <- apply(NA.test,1,sum,na.rm=T)


treaty$TREATY <- TREATY / TOTAL

treaty <- subset(treaty, select=c(ccode, year, TREATY))
names(treaty) <- c("COW", "YEAR","TREATY")


# load polity data
polity <- read.csv("p4v2010.csv", na.strings = c("-66", "-77", "-88", "-99"))
polity2 <- subset(polity, select=c(ccode, year, polity2))

# load updated Gleditsch trade gdp and population data
new.gdp <- read.delim("gdpv6.txt")
new.gdp <- subset(new.gdp, select=c(statenum, year, rgdppc, pop))
names(new.gdp) <- c("ccode", "year", "rgdpch", "pop") # rename because of the old name

# merge the data together and make lags
data <- merge(data, treaty, by.x=c("COW", "YEAR"), by.y=c("COW", "YEAR"), all.x=TRUE, all.y=FALSE, suffixes = c("",".treaty"))
nrow(data)

data <- merge(data, polity2, by.x=c("COW", "YEAR"), by.y=c("ccode", "year"), all.x=TRUE, all.y=FALSE)
nrow(data)

data <- merge(data, new.gdp, by.x=c("COW", "YEAR"), by.y=c("ccode", "year"), all.x=TRUE, all.y=FALSE)
nrow(data)
data.lag <- data
data.lag$YEAR <- data.lag$YEAR + 1
data <- merge(data, data.lag, by.x=c("COW", "YEAR"), by.y=c("COW", "YEAR"), all.x=TRUE, all.y=FALSE, suffixes = c("",".lag"))

data <- subset(data, !is.na(latentmean.lag) & !is.na(TREATY.lag) & !is.na(polity2.lag) & !is.na(rgdpch.lag) & !is.na(pop.lag) & YEAR>=year[j] & YEAR<=2010)
nrow(data)

newdata <- list()

# take n draws from the posterior distribution and make n new datasets in the list object just created
for(i in 1:1000){
  newdata[[i]] <- data
  newdata[[i]]$draw.lag <- rnorm(cbind(rep(1,nrow(data))), mean=data$latentmean.lag, sd=data$latentsd.lag)
  newdata[[i]]$draw <- rnorm(cbind(rep(1,nrow(data))), mean=data$latentmean, sd=data$latentsd)
}

# define model formula for each specification
FML <- "latentmean ~ draw.lag + TREATY.lag"
#FML <- "latentmean ~ draw.lag + TREATY.lag + polity2.lag"
#FML <- "latentmean ~ draw.lag + TREATY.lag + polity2.lag + log(rgdpch.lag)"
#FML <- "latentmean ~ draw.lag + TREATY.lag + polity2.lag + log(rgdpch.lag) + log(pop.lag)"
#FML <- "latentmean ~ draw.lag + TREATY.lag + log(rgdpch.lag) + log(pop.lag)"
#FML <- "latentmean ~ draw.lag + TREATY.lag + log(rgdpch.lag)"
#FML <- "latentmean ~ draw.lag + TREATY.lag + log(pop.lag)"
#FML <- "latentmean ~ draw.lag + TREATY.lag + polity2.lag + log(pop.lag)"


# define function to combine multe model estimates (this incorporates uncertatiny from the latent variables, see the supplementary appendix section F for more details about this function)
milm <- function(fml, midata){
 xx <- terms(as.formula(fml))
 lms <- matrix(data=NA, nrow=(length(attr(xx, "term.labels")) + 1), ncol=length(midata))
 ses <- matrix(data=NA, nrow=(length(attr(xx, "term.labels")) + 1), ncol=length(midata))
 vcovs <- list()
  for(i in 1:length(midata)){
    tmp <- lm(formula=as.formula(fml), data=midata[[i]])
    lms[,i] <- tmp$coefficients
    ses[,i] <- sqrt(diag(vcov(tmp)))
    vcovs[[i]] <- vcov(tmp)
  }
 par.est <- apply(lms, 1, mean)
 se.within <- apply(ses, 1, mean)
 se.between <- apply(lms, 1, var)
 se.est <- sqrt(se.within^2 + se.between*(1 + (1/length(midata))))
 list("terms"=names(tmp$coefficients), "beta" = par.est, "SE"=se.est, "vcovs"=vcovs,"coefs" = lms)    
}

# call the milm function with the list of datasets newdata
model <- milm(fml=FML, midata=newdata)

# create object for printing during the loop and then print the results to screen
temp <- data.frame(model$terms, model$beta, model$SE, model$beta/model$SE)

print(paste("Model Output"))
print(temp)

print(as.numeric(temp[temp$model.terms=="TREATY.lag",2]))
print(as.numeric(temp[temp$model.terms=="TREATY.lag",3]))
par(mar=c(5,7,5,5))

if(k==1)Dynamic.est <- as.numeric(temp[temp$model.terms=="TREATY.lag",2])   
if(k==1)Dynamic.se <- as.numeric(temp[temp$model.terms=="TREATY.lag",3])

if(k==2)Constant.est <- as.numeric(temp[temp$model.terms=="TREATY.lag",2])
if(k==2)Constant.se <-  as.numeric(temp[temp$model.terms=="TREATY.lag",3])

# barplot of the difference between treaty coefficient for models using the two different latent human rights variables from Fariss (2014)
if(k==2){
barplot(c(Constant.est, Dynamic.est), ylim=c(-0.2, 0.2), xlim=c(0,3), yaxt="n")
lines(c(.7,.7),c(Constant.est+1.96*Constant.se, Constant.est-1.96*Constant.se))
lines(c(1.9,1.9),c(Dynamic.est+1.96*Dynamic.se, Dynamic.est-1.96*Dynamic.se))
lines(c(.7,.7),c(Constant.est+1*Constant.se, Constant.est-1*Constant.se), lwd=3)
lines(c(1.9,1.9),c(Dynamic.est+1*Dynamic.se, Dynamic.est-1*Dynamic.se), lwd=3)
lines(c(2.9,2.9), c(Dynamic.est, Constant.est), lwd=2)
lines(c(2.75,2.9), c(Constant.est, Constant.est), lwd=2)
lines(c(2.75,2.9), c(Dynamic.est, Dynamic.est), lwd=2)
abline(h=0,col=grey(.75),lwd=1.5, lty=2)
lines(c(2.9,3.2), c(0,0), lwd=2)
if(FULL==TRUE)mtext(side=2, "Linear Model Coefficients for \nTotal Number of Ratified Treaties", line=3.5, cex=1.7, at=0)
if(FULL==FALSE)mtext(side=2, "Linear Model Coefficients for Total Ratified \nfrom CAT, CCPR, CESCR, CERD, CEDAW, CRC", line=3.5, cex=1.7, at=0)
mtext(side=4, "Difference\nBetween Estimates", line=2.25, cex=1.7, at=0)
mtext(side=3, "Model Coefficient\n(Dynamic Standard DV)", line=1.25, cex=1.7, at=1.9)
mtext(side=1, "Model Coefficient\n(Constant Standard DV)", line=3.00, cex=1.7, at=.7)
mtext(side=1, "DV=Latent Physical Integrity", line=3.00, cex=1.0, at=3)
axis(side=2, at=c(-0.2,-0.1,0,0.1,0.2), las=2)
# Z-score for difference
print(paste("Z-score for difference"))
print(Dynamic.est-Constant.est)

Z <- (Dynamic.est-Constant.est)/sqrt(Dynamic.se^2 + Constant.se^2)
print(2*(1-pnorm(Z)))

diffs[j] <- Dynamic.est-Constant.est
diffs.se[j] <- sqrt(Dynamic.se^2 + Constant.se^2)

beta1[j] <- Dynamic.est
beta2[j] <- Constant.est
se1[j] <- Dynamic.se
se2[j] <- Constant.se
}

}
}

# barplot of the differences across all 8 model specifications
# barplot of the differences across all 8 model specifications
par(mar=c(0,4,2,6))
par(mfrow=c(4,1))
barplot(beta1, space=0, ylim=c(-.25,.25), col=grey(.9), yaxt="n", border="white", xlim =c(1,60))
abline(h=0, col=grey(.5))
for(i in 1:7){abline(v=(c(2,12,22,32,42,52,62)-.5)[i], lwd=.75, col=grey(.75), lty=2)}
for(i in 1:length(year)){
    lines(c(i-.5,i-.5), c(beta1[i]+1.96*se1[i], beta1[i]-1.96*se1[i]), lwd=1)
    lines(c(i-.5,i-.5), c(beta1[i]+se1[i], beta1[i]-se1[i]), lwd=2)
}
#box()
#axis(side=1, at=c(.5:61.5), labels=rep("", 62))
axis(2, at=c(-.20, -.15, -.10, -.05, 0, .05, .10, .15, .20), las=2)
mtext(side=4, "Model Coefficient \nChanging Standard DV", line=3.0, cex=1.0)
mtext(side=3, "Human Rights Proportion \nTreaty Variable Coeffients and Differences in Coefficients", line=-1.5, cex=1.25)

par(mar=c(1,4,1,6))
barplot(beta2, space=0, ylim=c(-.25,.25), col=grey(.9), yaxt="n", border="white", xlim=c(1,60))
abline(h=0, col=grey(.5))
for(i in 1:7){abline(v=(c(2,12,22,32,42,52,62)-.5)[i], lwd=.75, col=grey(.75), lty=2)}
for(i in 1:length(year)){
    lines(c(i-.5,i-.5), c(beta2[i]+1.96*se2[i], beta2[i]-1.96*se2[i]), lwd=1)
    lines(c(i-.5,i-.5), c(beta2[i]+se2[i], beta2[i]-se2[i]), lwd=2)
}
#box()
#axis(side=1, at=c(.5:61.5), labels=rep("", 62))
axis(2, at=c(-.20, -.15, -.10, -.05, 0, .05, .10, .15, .20), las=2)
mtext(side=4, "Model Coefficient \nConstant Standard DV", line=3.0, cex=1.00)

par(mar=c(2,4,0,6))
barplot(diffs, space=0, ylim=c(-.25,.25), col=grey(.9), yaxt="n", border="white", xlim=c(1,60))
abline(h=0, col=grey(.5))
for(i in 1:7){abline(v=(c(2,12,22,32,42,52,62)-.5)[i], lwd=.75, col=grey(.75), lty=2)}
for(i in 1:length(year)){
    lines(c(i-.5,i-.5), c(diffs[i]+1.96*diffs.se[i], diffs[i]-1.96*diffs.se[i]), lwd=1)
    lines(c(i-.5,i-.5), c(diffs[i]+diffs.se[i], diffs[i]-diffs.se[i]), lwd=2)
}
#box()
axis(side=1, at=c(.5:61.5), labels=rep("", 62))
axis(1, at=c(2,12,22,32,42,52,62)-.5, label=c(1950, 1960, 1970, 1980, 1990, 2000, 2010), tick=F, cex.axis=1.5)
axis(2, at=c(-.20, -.15, -.10, -.05, 0, .05, .10, .15, .20), las=2)
mtext(side=4, "Difference \nBetween Estimates", line=3.0, cex=1.00)

par(mar=c(3,4,2,6))
barplot(-OBS, space=0, col=grey(.9), yaxt="n", border="white", xlim=c(1,60), ylim=c(-11000, 0))
TICKS <- c(0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000)
axis(2, at=-TICKS, labels=TICKS, las=2)
text(45, quantile(-OBS, .18), "Parameters based on two competing models. \nBoth competing models are estimated \nusing a sample of year t through 2010.", cex=1.5)
mtext(side=3, "Decreasing Sample Size Over Time", line=0, cex=1)
box()
